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Effective epileptic seizure detection based on the event-driven processing and machine learning for mobile healthcare

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Abstract

Mobile healthcare is a promising approach. It is realized by using the biomedical implants that are connected to the cloud. A framework for the precise and effective diagnosis of epileptic seizures is designed in this context. To achieve real-time compression and effective signal processing and transmission, it uses an intelligent event-driven electroencephalogram (EEG) signal acquisition. Experimental results show that grace of the event-driven nature an overall 3.3 fold compression and transmission bandwidth usage reduction is achieved by the devised method compared to the conventional counterparts. It promises a notable decrease in the post analysis and classification processing activity. The system performance is studied by using a standard three class EEG epileptic seizure dataset. The highest classification accuracy of 97.5% is secured for a mono-class. The best average classification accuracy of 96.4% is attained for three-classes. Comparison of the system with classical equivalents is made. Results demonstrate more than threefold and sevenfold of outperformance respectively in terms of compression gain and processing efficiency while confirming a comparable classification precision.

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Notes

  1. EEG time series are available under (https://www.meb.unibonn.de/epileptologie/science/physik/eegdata.html).

  2. https://www.cs.waikato.ac.nz/ml/weka/.

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Acknowledgement

Authors are thankful to anonymous reviewers for their valuable feedback.

Funding

This project is funded by the Effat University with the decision number UC#7/28Feb 2018/10.2-44i.

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  1. College of Engineering, Effat University, Jeddah, 22332, Saudi Arabia

    Saeed Mian Qaisar & Abdulhamit Subasi

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  1. Saeed Mian Qaisar
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  2. Abdulhamit Subasi
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Correspondence to Saeed Mian Qaisar.

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Mian Qaisar, S., Subasi, A. Effective epileptic seizure detection based on the event-driven processing and machine learning for mobile healthcare. J Ambient Intell Human Comput 13, 3619–3631 (2022). https://doi.org/10.1007/s12652-020-02024-9

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